ArticleIEEE transactions on pattern analysis and machine intelligence2023
Self-Supervised Learning of Graph Neural Networks: A Unified Review.
Article in IEEE transactions on pattern analysis and machine intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 352 citations in OpenAlex.
- Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review.Frontiers in oncology · 2025Pooled it
- Conditional Information-Bottleneck Graph Clustering for Structured Representation Learning in Dynamic Vehicular ISAC Networks.Entropy (Basel, Switzerland) · 2026Article
- Transformers for molecular property prediction: domain adaptation efficiently improves performance.Journal of cheminformatics · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Graph Neural Networks for Medical Imaging Analysis and Biological Data: Integrating Topology, Geometry, Radiomics, and Generative AI.Bioengineering (Basel, Switzerland) · 2026Review
- Hierarchical knowledge distillation framework for efficient node influence prediction in large-scale complex networks.Scientific reports · 2026Article
- High-precision classification of WCE-based gastrointestinal abnormality using a fusion deep learning approach.Scientific reports · 2026Article
- Six artificial intelligence innovation strategies applied to autism spectrum disorder research: A narrative review.Pediatric investigation · 2026Review
- Clustering single-cell multi-omics data via multi-subspace contrastive learning with structural smoothness.Briefings in bioinformatics · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- A Graph Contrastive Learning Method for Enhancing Genome Recovery in Complex Microbial Communities.Entropy (Basel, Switzerland) · 2025Article
- Bayesian Optimization with Gaussian Processes Assisted by Deep Learning for Material Designs.The journal of physical chemistry letters · 2025Article
- Multi-modal cross-domain self-supervised pre-training for fMRI and EEG fusion.Neural networks : the official journal of the International Neural Network Society · 2025Article
- MAEST: accurately spatial domain detection in spatial transcriptomics with graph masked autoencoder.Briefings in bioinformatics · 2025Article
- MAEMC-NET: a hybrid self-supervised learning method for predicting the malignancy of solitary pulmonary nodules from CT images.Frontiers in medicine · 2025Article
- STMGraph: spatial-context-aware of transcriptomes via a dual-remasked dynamic graph attention model.Briefings in bioinformatics · 2024Article
- Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction.Briefings in bioinformatics · 2024Article
- Article
- Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics.Briefings in bioinformatics · 2024Article
- To Compress or Not to Compress-Self-Supervised Learning and Information Theory: A Review.Entropy (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Deep models trained in supervised mode have achieved remarkable success on a variety of tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a new paradigm for making use of large amounts of unlabeled samples. SSL has achieved promising performance on natural language and image learning tasks. Recently, there is a trend to extend such success to graph data using graph neural networks (GNNs). In this survey, we provide a unified review of different ways of training GNNs using SSL. Specifically, we categorize SSL methods into contrastive and predictive models. In either category, we provide a unified framework for methods as well as how these methods differ in each component under the framework. Our unified treatment of SSL methods for GNNs sheds light on the similarities and differences of various methods, setting the stage for developing new methods and algorithms. We also summarize different SSL settings and the corresponding datasets used in each setting. To facilitate methodological development and empirical comparison, we develop a standardized testbed for SSL in GNNs, including implementations of common baseline methods, datasets, and evaluation metrics.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.